Method and system for process control

ABSTRACT

A method and system for process control. The control system can be operably coupled to a processing system. The control system can include control devices operably coupled to the processing system; a modeling module to provide a linear model based at least in part on the processing system; a computational module to provide controller algorithms associated with the control devices; a user interface module to present at a user interface controller information based at least in part on the linear model and the controller algorithms; and a separate coordination module for establishing communication between the modeling module, the computational module and the user interface module. One or more control signals can be provided to at least one of the control devices for controlling the processing system. In one embodiment, the modeling module can generate the linear model from a non-linear process.

FIELD OF THE INVENTION

This disclosure relates generally to process control systems and morespecifically to a method and system for process control.

BACKGROUND

Processing facilities, such as manufacturing plants, chemical plants andoil refineries, are typically managed using process control systems.Valves, pumps, motors, heating/cooling devices, and other industrialequipment typically perform actions needed to process materials in theprocessing facilities. Among other functions, the process controlsystems often manage the use of the industrial equipment in theprocessing facilities.

In conventional process control systems, controllers are often used tocontrol the operation of the industrial equipment in the processingfacilities. The controllers can typically monitor the operation of theindustrial equipment, provide control signals to the industrialequipment, and/or generate alarms when malfunctions are detected.Advanced controllers can often use model-based control techniques tocontrol the operation of the industrial equipment. Model-based controltechniques typically involve using models to analyze input data, wherethe models identify how the industrial equipment should be controlledbased on the input data being received.

Process control systems typically include one or more processcontrollers and input/output (I/O) devices communicatively coupled to atleast one workstation and to one or more field devices, such as throughanalog and/or digital buses. The field devices can include sensors(e.g., temperature, pressure and flow rate sensors), as well as otherpassive and/or active devices. The process controllers can receiveprocess information, such as field measurements made by the fielddevices, in order to implement a control routine based upon the controlmodel. Control signals can then be generated and sent to the industrialequipment to control the operation of the process.

Many process control systems also include an application station. Theapplication station can execute a software application that performsvarious process functions, such as maintenance management functions,diagnostic functions, monitoring functions, and safety-related functionsin the process control system, such as based on the control model.

The software application is typically customized to the particularprocess being controlled. The process control system application,database service, and runtime service are highly dependent on oneanother. Changes to the common data components can necessitate theclient application to be rebuilt. Process control software developersare required to closely coordinate the development of database, runtime,and system software so that the software is built and released in aunified manner. Also, since the modeling application is integrated intothe particular control software, reuse of effective models is oftenlimited because of the differences in the particular control that isbeing exerted on different processing facilities.

Accordingly, there is a need for a method and system for non-linearprocess control that is flexible. There is a further need for such amethod and system that facilitates and/or expedites development of thecontrol technique.

SUMMARY

The Summary is provided to comply with 37 C.F.R. § 1.73, requiring asummary of the invention briefly indicating the nature and substance ofthe invention. It is submitted with the understanding that it will notbe used to interpret or limit the scope or meaning of the claims.

A method and system for process control is provided. The method andsystem can include a modeling module and a computational module that arein communication with a user interface and one or more control devicesby way of a coordination module. The modeling module can provide a modelfor the process and the computational module can provide controlalgorithms associated with the control devices. The use of modules and,in particular, the coordination module, can provide flexibility indeveloping and implementing the control over the process. The method andsystem can implement control over non-linear systems, such as throughlinear models, linear piecewise models and/or non-linear models.

In one exemplary embodiment of the present disclosure, a method ofcontrolling a non-linear process is provided. The method can includeproviding a model based at least in part on the non-linear process, withthe model being provided by a modeling module of a control system;providing controller algorithms associated with control devices of thenon-linear process, with the controller algorithms being provided by acomputational module of the control system; presenting on a userinterface controller information based at least in part on the model andthe controller algorithms, with the user interface being provided by auser interface module; placing the modeling module, the computationalmodule and the user interface module in communication using acoordination module of the control system; and controlling thenon-linear process by sending one or more control signals from thecoordinating module to at least one of the control devices.

In another exemplary embodiment, a control system operably coupled to aprocessing system is provided. The control system can include controldevices operably coupled to the processing system; a modeling module toprovide a model based at least in part on the processing system; acomputational module to provide controller algorithms associated withthe control devices; a user interface module to present at a userinterface controller information based at least in part on the model andthe controller algorithms; and a coordination module for establishingcommunication between the modeling module, the computational module andthe user interface module. One or more control signals can be providedto at least one of the control devices for controlling the processingsystem. The modeling module and the computational module can operateutilizing different application platforms.

In a further exemplary embodiment, a computer-readable storage mediumcomprising computer-readable program instructions for controlling anon-linear process is provided. The program can include programinstructions for causing a computer to query a modeling module for amodel of the non-linear process; program instructions for causing thecomputer to query a computational module for controller algorithmsassociated with one or more control devices of the non-linear process;program instructions for causing the computer to present on a userinterface controller information based at least in part on the model andthe controller algorithms; and program instructions for causing thecomputer to send one or more control signals to at least one of the oneor more of the control devices to implement control of the non-linearprocess.

The technical effect includes, but is not limited to, allowing forinterchangeable platforms for development of the process control thatresults in an easier and/or expedited development process. The technicaleffect further includes, but is not limited to, decentralizingarchitecture that provides for reuse of components for the processcontrol system.

The above-described and other features and advantages of the presentdisclosure will be appreciated and understood by those skilled in theart from the following detailed description, drawings, and appendedclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of an exemplary system for processcontrol according to an embodiment of the present invention;

FIG. 2 is a schematic illustration of another exemplary system forprocess control according to an embodiment of the present inventioncoupled to a processing facility;

FIG. 3 is a schematic illustration of another exemplary system forprocess control according to an embodiment of the present inventioncoupled to a processing facility;

FIG. 4 is a schematic illustration of another exemplary system forprocess control according to an embodiment of the present inventioncoupled to a processing facility;

FIG. 5 is a schematic illustration of another exemplary system forprocess control according to an embodiment of the present invention; and

FIG. 6 is a flow chart illustrating an exemplary method for processcontrol according to an embodiment of the present invention using thesystems of FIGS. 1 through 5.

DETAILED DESCRIPTION OF THE INVENTION

Referring to the drawings, and in particular to FIG. 1, a control systemis shown and generally represented by reference numeral 10. Controlsystem 10 can be used with various processing facilities and variousprocesses, such as manufacturing processes, chemical plants and oilrefineries. The particular type of facility and the particular type ofprocess that is to be controlled is not intended to be limited. Controlsystem 10 can provide for control of a multi-variable process. In oneembodiment, the control system 10 can be applied to a non-linearprocess, but the present disclosure also contemplates the use of thecontrol system for implementing control in linear processes.

Control system 10 can include a modeling module 20, a computationalmodule 30, a user interface module 40, a coordination module 50, and anapplication server 60. The present disclosure also contemplates the useof other modules, including a plurality of modules for one or more ofthe modeling module 20, the computational module 30, the user interfacemodule 40, and the coordination module 50. For example, the modelingmodule 20 can be comprised of several different modules to developmodels based upon different events or potential events, or the userinterface module 40 can be comprised of several different modules fordisplaying different information associated with the control process.

Modeling module 20 can be used for providing and/or generating a modelrepresentative of the process being controlled. In one embodiment,modeling module 20 can generate a linear model from a non-linear processbeing controlled. The linear model can be of various types. In oneembodiment, the linear model can be a state space model. However, othermodels are also contemplated, such as neural or fuzzy models. Modelingmodule 20 can be various modeling platforms, such as gPROMS® (as shownin FIG. 1) or other platforms, including customized applications.

In one embodiment, the modeling module 20 can be used to generate areference trajectory for a grade transition of a product, such as anoptimum trajectory. For example, where a change of productspecifications is desired, a reference trajectory can be generated basedupon a profile of particular variables associated with the process, andthe control process can then be optimized by tracking the referencetrajectory.

Computational module 30 can be used for providing and/or generatingcontroller algorithms that are associated with control devices 250(shown in FIG. 2) operably coupled to the process facility andintegrated into the process being controlled. Computational module 30can be various computational platforms, such as MATLAB®, RobustMultivariable Predictive Control Technology (RMPCT) engines or otherapplication or native platforms, including customized applications.

User interface module 40 can be used for providing a user interface forthe control process. The user interface can present various controlinformation, including operator displays corresponding to processparameters, and trends for controller inputs and/or outputs. Theparticular type of controller information presented can vary based uponthe type of processing facility and the type of process beingcontrolled, such as feed rates and product molecular weights in apolymerization process. The user interface can also receive variouscommands or other inputs from the operator, such as a prediction requestfor proposed operating conditions or other proposed events. In oneembodiment, the user interface module 40 can provide for customizationof the presentation and/or retrieval of information, inputs, outputs,and the like. For example, the user interface module 40 can provide forthe operator to build their own displays of the controller informationso as to reduce the time and cost of the user interface development forthe control system 10. Previously developed user interfaces can beimplemented through use of the user interface module 40 to furtherfacilitate development of the control system 10. The user interfacemodule 40 can be various platforms, such as a Honeywell Experion®Process Knowledge System (EPKS) station, utilizing HMIweb. In oneembodiment, the user interface can be adjusted through use of the userinterface module 40 while the control system 10 is on-line.

The modeling module 20, the computational module 30, and the userinterface module 40 can be coupled by way of the coordination module 50.The coupling can be through use of various components and techniques,including hardwire, optical and/or wireless couplings. In oneembodiment, the coordination module 50 can be separate hardware from theother modules. In another embodiment, the coordination module 50 can bethe same hardware as the modules but different software.

The coordination module 50 can provide a real-time environment forcoupling of the other modules or components of the control system 10.The coordination module 50 can be a central coordinating block for thecontrol system 10. The coordination module 50 can interact with the userinterface module 40 (e.g., through use of HMIWeb) to retrieve operatorinputs and/or display the data to the operator user, including trendsfor controller data. In one embodiment, based on the user inputs, thecoordination module 50 can trigger the modeling module 20 and thecomputational module 30.

The coordination module 50 can include associated writeable memory,which is preferably non-volatile, to serve as a data repository forvarious variables, data or other information, such as storingoperational variables that have been determined based upon operationalparameters that were measured or otherwise sensed from the process beingcontrolled. Coordination module 50 can be various processing platformsand can include various interfaces and operate according to variousprotocols for communication with the other modules. In one embodiment,the modeling module 20 and the computational module 30 are run ondifferent application or native platforms. The coordination module 50can communicate with both of the different platforms for performing thecontrol process. In one embodiment, the coordination module 50 caninclude associated memory for storing controller parameters and/or modelparameters as points associated with the user interface of the userinterface module 40. In another embodiment where the user interfacemodule 40 comprises the Honeywell EPKS station, storing the controllerparameters and/or model parameters as points allows for the use ofHMIweb graphics.

The application server 60 can provide for the connection between thecoordination module 50 and the user interface module 40. In oneembodiment, the application server 60 can map different variablesgenerated between the coordination module 50 and the user interfacemodule 40. The application server 60 can also be the interface with theprocessing facility or other systems being controlled.

Referring to FIG. 2, where similar features are labeled by the samereference numerals as in FIG. 1, system 10 is shown coupled to aprocessing facility 200 by way of the application server 60. The presentdisclosure also contemplates other components and techniques forcoupling the control system 10 to the processing facility 200. Forexample, although not shown in FIG. 2, an intermediary component, suchas an interface module can be disposed between the application server 60and the processing facility 200.

The processing facility 200 can have one or more control devices 250integrated into the process. The control devices 250 can be variousdevices, including valves, pumps, motors, heating/cooling devices, andother industrial equipment, as well as sensors (e.g., temperature,pressure and flow rate sensors), and other passive and/or activedevices. The present disclosure is not intended to be limited by thetype of control devices that are used to implement the control of theprocess, and can include a variety of devices and combinations ofdevices, such as a sub-system to adjust pressure and/or temperature in aportion of the process.

The modeling module 20 can have a modeling tool 22, an optimizer 24, anda predictor 26. In one embodiment, modeling tool 22 is a lineariser (asshown in FIG. 2) that can provide or otherwise generate a linear model(e.g., a state space model), such as from a non-linear process, at therequest or query from the coordination module 50. In another embodiment,lineariser 22 can provide or otherwise generate a model that ispiece-wise linear. In yet another embodiment, modeling tool 22 cangenerate a non-linear model.

Optimizer 24 can optimize the control of the process, such as based atleast in part on one or more operational variables that are generatedfrom operational parameters, including measurements taken from controldevices 250 (e.g., sensors), as well as based upon a model for theprocess. For example, an operator can request controller informationrelated to a specific event, such as the changing of a product. Theoptimizer 24 can determine the optimum control conditions (e.g.,minimization of time or cost) for implementation of the specific event.

Predictor 26 can predict a state of the process based at least in parton one or more proposed variables, the model and the controlleralgorithms. For example, an operator can request or query a predictionfor the state of the process if run at a different flow rate, pressureand/or temperature. Through user interface module 40, controlinformation can be displayed associated with the predicted state, suchas product specifications (e.g., average molecular weight in the case ofa polymerization process).

In one embodiment, input files 52 can be provided by the coordinationmodule 50 to the modeling module 20. The input files 52 can be ofvarious formats, such as *.gOPT files if gPROMS® is the modelingplatform for module 20, and can include various data, such as statevariables, initial conditions, reference trajectory, system matrices,control matrices, output matrices, feed-forward matrices, and others.Output files 54 can be provided by the modeling module 20 to thecoordination module 50. The output files 54 can be of various formats,and can include various data, such as state space matrices, statevariables, intermediate variable and others. The coordination module 50can retrieve data from other sources, such as state variable initialconditions from a foreign object 58 that can be linked to a database.The coordination module 50 can also perform scheduling functions forqueries and the like, such as retrieving data and models.

Referring to FIG. 3, another exemplary embodiment of system 10 is shownwith the modeling module 20, the computational module 30, and the userinterface module 40 coupled by way of the coordination module 50. Inthis embodiment, the control system 10 has four separate systems 1-4which are in communication with each other through the coordinationmodule 50. The user interface module, such as the Experion® server cancommunicate directly with the process facility 200 and the coordinationmodule 50. Modeling functions, such as the optimizing, predicting and/orlinearising can be performed by the coordination module 50, while themodeling module 20 can utilize a modeling platform, such as gPROMS® toprovide updated trajectory variables to the coordination module 50.Various formats can be used for maintaining, manipulating and/orcommunicating data, such as XML files.

Referring to FIG. 4, another exemplary embodiment of system 10 is shownwith the modeling module 20, the computational module 30, and the userinterface module 40 coupled by way of the coordination module 50. Inthis embodiment, the control system 10 also has four separate systems1-4 which are in communication with each other through the coordinationmodule 50. The user interface module can be a Profit Suite™ OperatorStation that communicates indirectly with the process facility 200through the coordination module 50. Modeling functions, such as theoptimizing, predicting and/or linearising can be performed by thecoordination module 50, while the modeling module 20 can utilize amodeling platform, such as gPROMS® to provide updated trajectoryvariables to the coordination module 50. Various formats can be used formaintaining, manipulating and/or communicating data, such as XML files.

Referring to FIG. 5, another exemplary embodiment of system 10 is shownwith the modeling module 20 coupled with the coordination module 50.Modeling functions, such as the optimizing, predicting and/orlinearising can be performed by the coordination module 50, while themodeling module 20 can utilize a modeling platform, such as gPROMS® toprovide updated trajectory variables to the coordination module 50.Various formats can be used for maintaining, manipulating and/orcommunicating data, such as XML files.

An interfacing application can be used with the modeling module 20 thatcan be a wrapper for gPROMS® to interface with the coordination module50 as shown in FIG. 5. In one embodiment, all the communication betweenthe modeling module 20 and the coordination module 50 would pass throughthis application as an intermediate. A foreign object can be used forsupplying initial conditions (e.g., state variables) for gPROMS® usingOPC client to read data from the coordination module, as in the exampleof FIG. 5. A Foreign Process Interface can be used to send gPROMS®simulation results to the coordination module 50 using the OPC client.

An exemplary sequence of data communication or operations is shown bysequences 1 through 9 of FIG. 5. However, it should be understood thatother sequences of data communication or operations are contemplated bythe present disclosure. In sequence 1, there can be a call to gPROMS®for optimization. The input and output can be flat files. In sequence 2,the optimize call from the coordination module 50 can be passed togPROMS® by the interfacing application. In sequence 3, gPROMS® can usethe foreign object to obtain the initial conditions for optimizationactivity. The optimize operation can be completed at sequence 4. Insequence 5, there is a call to gPROMS® simulation. The interfacingapplication can forward the call to gPROMS® from the coordination module50. In sequence 6, gPROMS® can receive or retrieve the initialconditions from the coordination module 50 for the simulation activity.gPROMS® can use the foreign process interface to update the simulationresults to the coordination module 50 in sequence 7. There can be a callto the gPROMS® lineariser in sequence 8. The interfacing application canforward the call to gPROMS®. The results of the gPROMS® linearizationcan be sent back to the coordination module 50 by the interfacingapplication in sequence 9.

Referring to FIG. 6, a method for process control is shown and generallyrepresented by reference numeral 600. The description of the method 600is with respect to the features of system 10 as illustrated by way ofexample in FIGS. 1 and 2. In step 610, the control system 10 candetermine if there has been a parameter change requiring analysis. Theparameter change can be any event, either real or proposed, thatrequires use of the various modules (e.g., modeling module 20 andcomputation module 30) to provide information and/or adjust the controlfor the processing facility 200. In step 620, control system 10 candetermine if the change is an actual event.

If an actual event has occurred, then the coordination module 50 canquery the modeling module 20 for a model representing the currentprocess. In one embodiment the model is a linear or piecewise linearmodel, but non-linear models may also be provided. In step 630, themodeling module 20 can generate a model. Based at least in part on themodel, as well as controller algorithms associated with the model thatwere generated by the computational module 30, the coordination module50 can present controller information to the user interface module 40,as in step 640. For example, where an event has occurred that requiresan increase in the amount of a particular chemical component to be addedto the reactant to maintain a desired specification, the controllerinformation can display the need for a flow rate increase of theparticular chemical composition.

In step 650, one or more of the controller devices 250 can be adjustedto implement a particular control technique that has been determinedbased upon the event, the model and the controller algorithms. In oneembodiment, the adjustment can be in response to a confirmation signalsent from the operator based on receipt of the controller informationdescribed with respect to step 640. Other adjustment techniques can alsobe used, such as automatically making certain adjustments that fallwithin a first category of adjustments and requiring confirmation of theadjustment for certain adjustments that fall within a second category ofadjustments.

If an actual event has not occurred, then the coordination module 50 canquery the modeling module 20 for a model (e.g., linear, piece-wiselinear or non-linear) representing the process as proposed by theoperator (e.g., a change in one or more operating parameters or a changeof the product). Determination of the occurrence of an event can be doneby various techniques and components, including monitoring throughsensors or distinguishing operator inputs from process inputs.

In step 635, the modeling module 30 can provide or other wise generatethe model (e.g., linear, piecewise linear or non-linear) and thenutilize the optimizer 24 or the predictor 26 for generating controllerinformation associated with the proposed state of the process. Based atleast in part on the model, as well as controller algorithms associatedwith the model that were generated by the computational module 30, thecoordination module 50 can present the controller information to theuser interface module 50, as in step 645. For example, where an operatordesires to change a product to have different specifications (e.g.,different ratio of chemical components or different average molecularweight), the user interface module 40 can present controller informationassociated with the controller device adjustments that would optimizethe change (e.g., reduce time or losses). As another example, where anoperator desires to know the impact of an increased temperature formixing of chemical components, the user interface module 40 can presentcontroller information associated with the resulting product (e.g.,product specifications).

Control system 10 can provide a flexible software architecture for rapiddevelopment of a controller product, such as a non-linear controllerthat utilizes a linear model. In one embodiment, the controller deviceexecution can take place in the computational module 30 or in thecoordination module 50. Control system 10 can provide for the operatorto develop models and/or algorithms in their native platforms throughuse of the modeling module 20 and/or the computational module 30,respectively. The native or programming platforms can be different, suchas using different programming languages or being distinct software. Forexample, the MATLAB® environment is often used for developing controllerand computational algorithms and is a distinct application platform fromthe gPROMS® modeling tool.

Modeling module 20 can provide for verified platforms to be utilized ingenerating linear models, such as gPROMS® which is a tested product formodel development. This further can eliminate the need for a separatephase to convert the developed models or algorithms to C/C++, whichreduces the product cycle development efforts. The use of coordinationmodule 50 allows a convenient architecture to have multiple computernodes for loading and/or installing any required software.

The use of the real-time environment of the coordination module 50 toinitiate modeling/controller tasks allows a simplified procedure forchanging the underlying components that complete these tasks. Forexample, a gPROMS® modeling task can be replaced with a customizedapplication at a later stage without making major modifications to theexisting architecture because the modeling tool 22 alone can bereplaced, such as the lineariser 22 shown in FIG. 2, rather thanreplacing the entire control system 10 or even the entire modelingmodule 20. Similarly, for example, controller algorithms in MATLAB® canbe replaced with a RMPCT engine with only a software change.

The present disclosure contemplates the use of a computer system withinwhich a set of instructions, when executed, may cause the machine toperform any one or more of the methodologies discussed above. Thecomputer instructions can be embodied in a storage medium. In someembodiments, the machine operates as a standalone device. In someembodiments, the machine may be connected (e.g., using a network) toother machines. In a networked deployment, the machine may operate inthe capacity of a server or a client user machine in server-client usernetwork environment, or as a peer machine in a peer-to-peer (ordistributed) network environment. The machine may comprise a servercomputer, a client user computer, a personal computer (PC), a tablet PC,a laptop computer, a desktop computer, a control system, a networkrouter, switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that machine. Further, the term “machine” shall be taken to include asingle machine or any collection of machines that individually orjointly execute a set (or multiple sets) of instructions to perform anyone or more of the methodologies discussed herein.

The computer-readable storage medium can have stored thereon one or moresets of instructions (e.g., software) embodying any one or more of themethodologies or functions described herein, including those methodsillustrated above. The computer-readable storage medium can be anelectromechanical medium such as a common disk drive, or a mass storagemedium with no moving parts such as Flash or like non-volatile memories.The instructions may also reside, completely or at least partially,within a main memory, a static memory, and/or within a processor duringexecution thereof by the computer system. The main memory and theprocessor also may constitute computer-readable storage media.

Dedicated hardware implementations including, but not limited to,application specific integrated circuits, programmable logic arrays andother hardware devices can likewise be constructed to implement themethods described herein. Applications that may include the apparatusand systems of various embodiments broadly include a variety ofelectronic and computer systems. Some embodiments implement functions intwo or more specific interconnected hardware modules or devices withrelated control and data signals communicated between and through themodules, or as portions of an application-specific integrated circuit.Thus, the example system is applicable to software, firmware, andhardware implementations.

In accordance with various embodiments of the present disclosure, themethods described herein are intended for operation as software programsrunning on a computer processor. Furthermore, software implementationscan include, but not limited to, distributed processing orcomponent/object distributed processing, parallel processing, or virtualmachine processing can also be constructed to implement the methodsdescribed herein. The present disclosure contemplates a machine readablemedium containing instructions, or that which receives and executesinstructions from a propagated signal so that a device, such asconnected to a network environment can send or receive data, and tocommunicate over the network using the instructions.

While the computer-readable storage medium can be a single medium, theterm “computer-readable storage medium” should be taken to include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore sets of instructions. The term “computer-readable storage medium”shall also be taken to include any medium that is capable of storing,encoding or carrying a set of instructions for execution by the machineand that cause the machine to perform any one or more of themethodologies of the present disclosure. The term “computer-readablestorage medium” shall accordingly be taken to include, but not belimited to: solid-state memories such as a memory card or other packagethat houses one or more read-only (non-volatile) memories, random accessmemories, or other re-writable (volatile) memories; magneto-optical oroptical medium such as a disk or tape; and carrier wave signals such asa signal embodying computer instructions in a transmission medium;and/or a digital file attachment to e-mail or other self-containedinformation archive or set of archives is considered a distributionmedium equivalent to a tangible storage medium. Accordingly, thedisclosure is considered to include any one or more of acomputer-readable storage medium or a distribution medium, as listedherein and including art-recognized equivalents and successor media, inwhich the software implementations herein are stored.

The illustrations of embodiments described herein are intended toprovide a general understanding of the structure of various embodiments,and they are not intended to serve as a complete description of all theelements and features of apparatus and systems that might make use ofthe structures described herein. Many other embodiments will be apparentto those of skill in the art upon reviewing the above description. Otherembodiments may be utilized and derived therefrom, such that structuraland logical substitutions and changes may be made without departing fromthe scope of this disclosure. Figures are also merely representationaland may not be drawn to scale. Certain proportions thereof may beexaggerated, while others may be minimized. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense. Thus, although specific embodiments have beenillustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description. Therefore, it is intended thatthe disclosure not be limited to the particular embodiment(s) disclosedas the best mode contemplated for carrying out this invention, but thatthe invention will include all embodiments falling within the scope ofthe appended claims.

The Abstract of the Disclosure is provided to comply with 37 C.F.R.§1.72(b), requiring an abstract that will allow the reader to quicklyascertain the nature of the technical disclosure. It is submitted withthe understanding that it will not be used to interpret or limit thescope or meaning of the claims.

1. A method of controlling a non-linear process, the method comprising: providing a model based at least in part on the non-linear process, the model being provided by a modeling module of a control system; providing controller algorithms associated with control devices of the non-linear process, the controller algorithms being provided by a computational module of the control system; presenting on a user interface controller information based at least in part on the model and the controller algorithms, the user interface being provided by a user interface module; placing the modeling module, the computational module and the user interface module in communication using a separate coordination module of the control system operably coupled to the modeling module, the computational module and the user interface module; and controlling the non-linear process by sending one or more control signals from the coordinating module to at least one of the control devices.
 2. The method of claim 1, wherein the model is a linear model or a piecewise linear model.
 3. The method of claim 2, wherein the model is generated by the modeling module.
 4. The method of claim 1, wherein the modeling module and the computational module operate using different application platforms.
 5. The method of claim 1, further comprising the step of replacing the model with another model generated by another modeling module, wherein the modeling module is replaced by the another modeling module, and wherein the another modeling module is placed in communication with the computational module and the user interface module using the coordination module of the control system.
 6. The method of claim 1, further comprising the step of replacing the controller algorithms with other controller algorithms associated with the control devices provided by another computational module, wherein the computational module is replaced by the another computational module, and wherein the another computational module is placed in communication with the modeling module and the user interface module using the coordination module of the control system.
 7. The method of claim 1, further comprising the step of: monitoring operational parameters associated with the non-linear process; generating one or more operational variables based at least in part on the operational parameters; and storing the one or more operational variables in the coordination module.
 8. The method of claim 7, wherein the control devices are selected from the group consisting of a valve, a switch, a motor, a heating apparatus, a cooling apparatus, a temperature sensor, a pressure sensor, and any combinations thereof.
 9. The method of claim 7, further comprising the step of communicating the one or more operational variables and the control information between the user interface module and the coordination module using an application server.
 10. The method of claim 7, wherein the modeling module optimizes the controlling of the non-linear process based at least in part on the one or more operational variables, the model and the controller algorithms.
 11. The method of claim 1, wherein the modeling module predicts a state of the non-linear process based at least in part on one or more proposed variables, the model and the controller algorithms.
 12. The method of claim 1, wherein the modeling module generates an optimum trajectory for a grade transition of the non-linear process.
 13. A control system for controlling processing systems, the control system comprising: a plurality of control devices operably coupled to a processing system for controlling a process of the processing system; a modeling module to provide a model based at least in part on the process; a computational module to provide controller algorithms associated with the plurality of control devices; a user interface module to present at a user interface controller information based at least in part on the model and the controller algorithms; and a separate coordination module for establishing communication between the modeling module, the computational module and the user interface module, wherein one or more control signals are provided to at least one of the plurality of control devices for the controlling of the process, and wherein the modeling module and the computational module operate using different application platforms.
 14. The control system of claim 13, wherein the model is a linear model or piecewise linear model, and wherein the modeling module transforms a non-linear process of the processing system to the linear model or piecewise linear model.
 15. The control system of claim 13, wherein said processing system comprises one or more sensors that send operational parameters to the coordination module, wherein one or more operational variables are generated based at least in part on the operational parameters, and wherein the one or more operational variables are stored in the coordination module.
 16. The control system of claim 15, wherein the modeling module optimizes the controlling of the processing system based at least in part on the one or more operational variables, the model and the controller algorithms, and wherein the modeling module predicts a state of the processing system based at least in part on one or more proposed variables, the model and the controller algorithms.
 17. The control system of claim 15, wherein the control devices are selected from the group consisting of a valve, a switch, a motor, a heating apparatus, a cooling apparatus, and any combinations thereof.
 18. A computer-readable storage medium comprising computer-readable program instructions for controlling a non-linear process, said program comprising: program instructions for causing a computer to query a modeling module for a model of the non-linear process; program instructions for causing said computer to query a computational module for controller algorithms associated with one or more control devices of the non-linear process; program instructions for causing said computer to present on a user interface controller information based at least in part on the model and the controller algorithms; and program instructions for causing said computer to send one or more control signals to at least one of the one or more control devices to implement control of the non-linear process.
 19. The storage medium of claim 18, further comprising: program instructions for causing said computer to generate a reference trajectory for a grade transition for the non-linear process, wherein the model is a linear model or piecewise linear model.
 20. The storage medium of claim 18, further comprising: program instructions for causing said computer to store one or more operational variables in a coordination module, wherein the operational variables are generated based at least in part on operational parameters sensed by one or more sensors operably coupled to the non-linear process; and program instructions for causing said computer to perform at least one of optimization and prediction, wherein optimization comprises optimizing the control of the non-linear process based at least in part on the one or more operational variables, the model and the controller algorithms, and wherein prediction comprises predicting a state of the non-linear process based at least in part on one or more proposed variables, the model and the controller algorithms. 